Cardiac arrhythmia analysis and characterization is used in the management of cardiac disorders and irregularities. Cardiac electrophysiological (EP) activities are used to sense, monitor and diagnose cardiac arrhythmia and pathology related abnormality. Such activities include, for example, P wave disorders for atrial fibrillation (AF) and ST segment changes for myocardial ischemia and infarction. However, known cardiac arrhythmia identification and analysis based on ECG signals is subjective and typically requires extensive clinical user expertise for accurate data interpretation and appropriate cardiac rhythm management. Coronary Artery Disease (CAD) and heart-related problems and cardiac arrhythmias are frequently fatal illnesses. A 12-lead electrocardiogram (ECG) and multi-channel intra-cardiac electrograms (ICEG) are a diagnostic reference standard for evaluating cardiac rhythm and events. Known waveform morphology and time domain parameter analysis of heart depolarization and repolarization, typically uses a P wave, QRS complex, ST segment or T wave, for cardiac arrhythmia monitoring and identification, e.g. atrial fibrillation (AF), myocardial ischemia (MI), ventricular tachycardia/fibrillation (VT/VF). However, the waveform morphologies and time domain parameter analysis may be subjective and time-consuming, and requires extensive expertise and clinical experience for accurate interpretation and proper cardiac rhythm management.
Some known systems apply sophisticated mathematical theories to biomedical signal interpretation, such as frequency analysis, symbolic complexity analysis and signal entropy evaluation, and focus on generating a pathology index for qualitative cardiac arrhythmia characterization. These systems typically do not determine data variance and statistical characteristics of time varying signals (ECG and ICEG). Additionally, cardiac electrophysiological activities and signals (ECG and ICEG) are time varying and known signal calculation and related analysis typically cannot localize a precise time and trend of cardiac events, such as arrhythmia occurrence.
Known cardiac signal diagnosis and interpretation systems based on an EP signal waveform and morphology, require extensive clinical knowledge and experience and provide inaccurate and subjective evaluation and diagnosis that may cause delay in cardiac rhythm management, such as drug delivery and emergency treatment. Known diagnosis and evaluation of a cardiac signal, especially ECG signals, usually use only time domain characteristics, such as voltage amplitude (e.g., ST elevation) and signal latency. Further, known system analysis of time domain parameters of an EP signal (such as a surface ECG signal), may fail to accurately characterize cardiac events or pathologies. ECG has been utilized for cardiac monitoring and is a standard clinical procedure but known ICEG signal diagnosis is not efficient when based on waveform changes, for example, since there is a lack of criteria or standards for amplitude or morphology diagnosis of ICEG signals for unipolar or bipolar cardiac activities. Time and frequency analysis may be utilized to diagnose abnormality of heart rhythms, but the analysis and monitoring fails to provide detailed information about the local myocardial tissue, such as instantaneous energy flow and instantaneous time varying complexity patterns. A system according to invention principles addresses these deficiencies and related problems.